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Financial analysis, algorithmic trading, portfolio optimization examples with Python (DISCLAIMER - No Investment Advice Provided, YASAL UYARI - Yatırım tavsiyesi değildir).

License: GNU General Public License v3.0

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quantitative-finance python quant quantitative-trading stock-market stock-analysis portfolio-optimization market-data

quantitavefinanceexamplespy's Introduction

QuantitaveFinanceExamplesPy

Financial analysis, algorithmic trading, portfolio optimization examples with Python

DISCLAIMER - No Investment Advice Provided

YASAL UYARI - Burada yer alan yatırım bilgi, yorum ve tavsiyeleri yatırım danışmanlığı kapsamında değildir.

Requirements

Please install requirements from requirements.txt.

References (for both methods and some code fragments)

  • Hilpisch, Y. J. (2021). Python for algorithmic trading: From idea to cloud deployment. O'Reilly.
  • Jansen, S. (2020). Machine learning for algorithmic trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with python. Packt Publishing.
  • Pik, J., & Ghosh, S. (2021). Hands-on financial trading with python. Packt Publishing.
  • Velu, R. P., Hardy, M., & Nehren, D. (2020). Algorithmic trading and quantitative strategies. CRC Press, Taylor & Francis Group.
  • Brugiere, P. (2021). Quantitative portfolio management: With applications in python. Springer Nature.
  • Dowd, K. (2007). Measuring market risk. John Wiley & Sons.
  • Hilpisch, Y. J. (2020). Artificial Intelligence in Finance. O'Reilly.

Contact

Murat Koptur, LinkedIn

Email: [email protected]

Examples

Note: In all examples, assumed the risk-free rate is zero.

Calculation Alpha and Beta factors

01_01

Cointegration

ARCLK.IS and TOASO.IS has cointegration, p-value: 0.04903369798110527
AYGAZ.IS and KCHOL.IS has cointegration, p-value: 0.007029900251131765
FROTO.IS and MAALT.IS has cointegration, p-value: 0.015757028038897322
FROTO.IS and OTKAR.IS has cointegration, p-value: 0.004399007493986555
KCHOL.IS and AYGAZ.IS has cointegration, p-value: 0.007101145930953294
MAALT.IS and FROTO.IS has cointegration, p-value: 0.00783799297255268
OTKAR.IS and FROTO.IS has cointegration, p-value: 0.003094678911810982
OTKAR.IS and TTRAK.IS has cointegration, p-value: 0.04185601871282213
OTKAR.IS and YKGYO.IS has cointegration, p-value: 0.00282083357242191
TTRAK.IS and OTKAR.IS has cointegration, p-value: 0.03639137062922606
TTRAK.IS and YKGYO.IS has cointegration, p-value: 0.03834839887528665
YKGYO.IS and OTKAR.IS has cointegration, p-value: 0.0017665073676291331
YKGYO.IS and TOASO.IS has cointegration, p-value: 0.046004150077470406
YKGYO.IS and TTRAK.IS has cointegration, p-value: 0.027200620035757236

PCA on Returns

03_01

Volatility calculations

Std.Dev. Estimator:          0.16988244687319595
Classical Estimator:         0.0013349197336295028
Rogers - Satchell Estimator: 0.0009643228704150725
Yang - Zang estimator:       0.0016329397449278639

Volatility-Volume Relationship

05_01

AR-ARCH models for volatility

06_01

VWAP

07_01

Technical Indicators

08_01

Denoising Data

09_01

Trading Signals

10_01 10_02

Backtesting

11_01

Pairs Trading

12_02

Modern Portfolio Theory - Efficient Frontier

13_01

Value-At-Risk - Expected Shortfall

14_01

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